{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AI股票拟合算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api('84a7d42ba53be1e345b15650d19116113087ad5440320b4c6b7b6c67')\n",
    "\n",
    "# 拉取数据\n",
    "data = pro.daily(**{\n",
    "    \"ts_code\": '000768.sz',\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": \"2000-1-1\",\n",
    "    \"end_date\": \"\",\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"close\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"vol\",\n",
    "    \"ts_code\"\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close        vol\n",
      "0     000768.SZ   20211105  32.58  33.15  32.24  32.24  322016.02\n",
      "1     000768.SZ   20211104  31.80  32.86  31.53  32.70  430820.20\n",
      "2     000768.SZ   20211103  31.51  32.04  30.92  31.82  353935.70\n",
      "3     000768.SZ   20211102  31.81  32.70  31.42  31.98  533900.05\n",
      "4     000768.SZ   20211101  30.81  31.82  30.25  31.45  489053.88\n",
      "...         ...        ...    ...    ...    ...    ...        ...\n",
      "4995  000768.SZ   20000719  13.50  13.50  13.20  13.24   13003.00\n",
      "4996  000768.SZ   20000718  12.91  13.48  12.85  13.46   21509.00\n",
      "4997  000768.SZ   20000717  12.60  12.95  12.58  12.86    8418.00\n",
      "4998  000768.SZ   20000714  12.76  12.77  12.50  12.58   11914.00\n",
      "4999  000768.SZ   20000713  12.79  12.85  12.60  12.70   10267.00\n",
      "\n",
      "[5000 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)\n",
    "data.to_csv(\"stock000768.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>42.89</td>\n",
       "      <td>43.88</td>\n",
       "      <td>42.59</td>\n",
       "      <td>43.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-28</th>\n",
       "      <td>44.00</td>\n",
       "      <td>44.78</td>\n",
       "      <td>42.38</td>\n",
       "      <td>42.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-27</th>\n",
       "      <td>45.45</td>\n",
       "      <td>45.52</td>\n",
       "      <td>43.50</td>\n",
       "      <td>44.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-26</th>\n",
       "      <td>44.51</td>\n",
       "      <td>46.23</td>\n",
       "      <td>44.14</td>\n",
       "      <td>45.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-25</th>\n",
       "      <td>44.41</td>\n",
       "      <td>44.95</td>\n",
       "      <td>43.57</td>\n",
       "      <td>44.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>974 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "trade_date                            \n",
       "2021-10-29  42.89  43.88  42.59  43.83\n",
       "2021-10-28  44.00  44.78  42.38  42.89\n",
       "2021-10-27  45.45  45.52  43.50  44.57\n",
       "2021-10-26  44.51  46.23  44.14  45.53\n",
       "2021-10-25  44.41  44.95  43.57  44.73\n",
       "...           ...    ...    ...    ...\n",
       "2017-10-31  23.71  23.71  23.71  23.71\n",
       "2017-10-30  21.55  21.55  21.55  21.55\n",
       "2017-10-27  19.59  19.59  19.59  19.59\n",
       "2017-10-26  17.81  17.81  17.81  17.81\n",
       "2017-10-25  13.49  16.19  13.49  16.19\n",
       "\n",
       "[974 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"./stock603722.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import akshare as ak\n",
    "\n",
    "pingan = ak.stock_zh_a_daily(symbol=\"sh601318\", adjust=\"qfq\")\n",
    "df3 = pingan.reset_index().iloc[-30:,:6]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"中国平安\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=90, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-26</th>\n",
       "      <td>21.75</td>\n",
       "      <td>22.19</td>\n",
       "      <td>21.18</td>\n",
       "      <td>21.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-25</th>\n",
       "      <td>20.08</td>\n",
       "      <td>21.71</td>\n",
       "      <td>20.08</td>\n",
       "      <td>21.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-22</th>\n",
       "      <td>21.83</td>\n",
       "      <td>22.04</td>\n",
       "      <td>20.73</td>\n",
       "      <td>20.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>21.62</td>\n",
       "      <td>22.91</td>\n",
       "      <td>21.20</td>\n",
       "      <td>22.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>20.71</td>\n",
       "      <td>21.93</td>\n",
       "      <td>20.11</td>\n",
       "      <td>21.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-28</th>\n",
       "      <td>16.50</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.31</td>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-27</th>\n",
       "      <td>16.47</td>\n",
       "      <td>16.72</td>\n",
       "      <td>16.31</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-26</th>\n",
       "      <td>16.06</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.01</td>\n",
       "      <td>16.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-25</th>\n",
       "      <td>16.20</td>\n",
       "      <td>16.35</td>\n",
       "      <td>15.81</td>\n",
       "      <td>16.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-22</th>\n",
       "      <td>17.78</td>\n",
       "      <td>17.88</td>\n",
       "      <td>16.50</td>\n",
       "      <td>16.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4776 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "trade_date                            \n",
       "2021-10-26  21.75  22.19  21.18  21.24\n",
       "2021-10-25  20.08  21.71  20.08  21.68\n",
       "2021-10-22  21.83  22.04  20.73  20.92\n",
       "2021-10-21  21.62  22.91  21.20  22.39\n",
       "2021-10-20  20.71  21.93  20.11  21.61\n",
       "...           ...    ...    ...    ...\n",
       "2000-12-28  16.50  16.68  16.31  16.31\n",
       "2000-12-27  16.47  16.72  16.31  16.50\n",
       "2000-12-26  16.06  16.58  16.01  16.42\n",
       "2000-12-25  16.20  16.35  15.81  16.05\n",
       "2000-12-22  17.78  17.88  16.50  16.59\n",
       "\n",
       "[4776 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    trade_date   open   high    low  close\n",
      "0   2021-10-29  42.89  43.88  42.59  43.83\n",
      "1   2021-10-28  44.00  44.78  42.38  42.89\n",
      "2   2021-10-27  45.45  45.52  43.50  44.57\n",
      "3   2021-10-26  44.51  46.23  44.14  45.53\n",
      "4   2021-10-25  44.41  44.95  43.57  44.73\n",
      "..         ...    ...    ...    ...    ...\n",
      "969 2017-10-31  23.71  23.71  23.71  23.71\n",
      "970 2017-10-30  21.55  21.55  21.55  21.55\n",
      "971 2017-10-27  19.59  19.59  19.59  19.59\n",
      "972 2017-10-26  17.81  17.81  17.81  17.81\n",
      "973 2017-10-25  13.49  16.19  13.49  16.19\n",
      "\n",
      "[974 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 974 entries, 973 to 0\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  974 non-null    datetime64[ns]\n",
      " 1   open        974 non-null    float64       \n",
      " 2   high        974 non-null    float64       \n",
      " 3   low         974 non-null    float64       \n",
      " 4   close       974 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 45.7 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2017-10-25</td>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>19.770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-25</td>\n",
       "      <td>44.41</td>\n",
       "      <td>44.95</td>\n",
       "      <td>43.57</td>\n",
       "      <td>44.73</td>\n",
       "      <td>44.864</td>\n",
       "      <td>44.625</td>\n",
       "      <td>47.564000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>44.51</td>\n",
       "      <td>46.23</td>\n",
       "      <td>44.14</td>\n",
       "      <td>45.53</td>\n",
       "      <td>44.954</td>\n",
       "      <td>44.884</td>\n",
       "      <td>47.475333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>45.45</td>\n",
       "      <td>45.52</td>\n",
       "      <td>43.50</td>\n",
       "      <td>44.57</td>\n",
       "      <td>44.808</td>\n",
       "      <td>45.018</td>\n",
       "      <td>47.328667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>44.00</td>\n",
       "      <td>44.78</td>\n",
       "      <td>42.38</td>\n",
       "      <td>42.89</td>\n",
       "      <td>44.426</td>\n",
       "      <td>44.869</td>\n",
       "      <td>47.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>42.89</td>\n",
       "      <td>43.88</td>\n",
       "      <td>42.59</td>\n",
       "      <td>43.83</td>\n",
       "      <td>44.310</td>\n",
       "      <td>44.664</td>\n",
       "      <td>46.711000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>974 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    trade_date   open   high    low  close       5      10         30\n",
       "973 2017-10-25  13.49  16.19  13.49  16.19     NaN     NaN        NaN\n",
       "972 2017-10-26  17.81  17.81  17.81  17.81     NaN     NaN        NaN\n",
       "971 2017-10-27  19.59  19.59  19.59  19.59     NaN     NaN        NaN\n",
       "970 2017-10-30  21.55  21.55  21.55  21.55     NaN     NaN        NaN\n",
       "969 2017-10-31  23.71  23.71  23.71  23.71  19.770     NaN        NaN\n",
       "..         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "4   2021-10-25  44.41  44.95  43.57  44.73  44.864  44.625  47.564000\n",
       "3   2021-10-26  44.51  46.23  44.14  45.53  44.954  44.884  47.475333\n",
       "2   2021-10-27  45.45  45.52  43.50  44.57  44.808  45.018  47.328667\n",
       "1   2021-10-28  44.00  44.78  42.38  42.89  44.426  44.869  47.045000\n",
       "0   2021-10-29  42.89  43.88  42.59  43.83  44.310  44.664  46.711000\n",
       "\n",
       "[974 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df3 = data.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"中航西飞\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(100)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,1100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-10-31', '2018-02-28', '2018-08-31', '2018-10-31',\n",
       "       '2019-02-28', '2019-04-30', '2019-10-31', '2019-12-31',\n",
       "       '2020-04-30', '2020-06-30', '2020-08-31', '2020-12-31',\n",
       "       '2021-04-30', '2021-06-30', '2021-08-31'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a=np.linspace(0,round(len(df3),0),10,dtype=int).tolist()\n",
    "b=df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "c=data.asfreq(\"2M\").dropna()\n",
    "#c.dropna().index\n",
    "cc=c.index.strftime('%Y-%m-%d').to_numpy()\n",
    "e=df3.trade_date.tolist()\n",
    "#f=[e[i] for i in cc]\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=data.iloc[::-1]\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          21.75  22.19\n",
       "1          20.08  21.71\n",
       "2          21.83  22.04\n",
       "3          21.62  22.91\n",
       "4          20.71  21.93\n",
       "...          ...    ...\n",
       "4771       16.50  16.68\n",
       "4772       16.47  16.72\n",
       "4773       16.06  16.58\n",
       "4774       16.20  16.35\n",
       "4775       17.78  17.88\n",
       "\n",
       "[4776 rows x 2 columns]>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19.15, 18.92, 16.92, 17.47, 17.95, 15.89, 15.34, 15.04, 15.45,\n",
       "       16.34, 17.14, 18.1 , 17.4 , 18.19, 18.32, 18.25, 17.27, 16.94,\n",
       "       16.64, 17.08, 17.15, 17.87, 18.23, 19.88, 21.87, 23.56, 23.17,\n",
       "       22.67, 21.59, 22.44, 21.88, 23.43, 25.77, 27.74, 27.51, 29.39,\n",
       "       29.55, 29.55, 30.5 , 31.  , 29.84, 29.66, 27.47, 27.  , 24.85,\n",
       "       23.58, 22.63, 22.88, 22.  , 21.79, 20.86, 20.52, 20.5 , 20.63,\n",
       "       22.  , 21.93, 22.91, 22.04, 21.71, 22.19])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.iloc[0:3000,1:3].values[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "ind=np.arange(0,3000)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17464.0</th>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "      <td>13.49</td>\n",
       "      <td>16.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17465.0</th>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "      <td>17.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17466.0</th>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "      <td>19.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17469.0</th>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "      <td>21.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17470.0</th>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18925.0</th>\n",
       "      <td>44.41</td>\n",
       "      <td>44.95</td>\n",
       "      <td>43.57</td>\n",
       "      <td>44.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18926.0</th>\n",
       "      <td>44.51</td>\n",
       "      <td>46.23</td>\n",
       "      <td>44.14</td>\n",
       "      <td>45.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18927.0</th>\n",
       "      <td>45.45</td>\n",
       "      <td>45.52</td>\n",
       "      <td>43.50</td>\n",
       "      <td>44.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18928.0</th>\n",
       "      <td>44.00</td>\n",
       "      <td>44.78</td>\n",
       "      <td>42.38</td>\n",
       "      <td>42.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18929.0</th>\n",
       "      <td>42.89</td>\n",
       "      <td>43.88</td>\n",
       "      <td>42.59</td>\n",
       "      <td>43.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>974 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "17464.0  13.49  16.19  13.49  16.19\n",
       "17465.0  17.81  17.81  17.81  17.81\n",
       "17466.0  19.59  19.59  19.59  19.59\n",
       "17469.0  21.55  21.55  21.55  21.55\n",
       "17470.0  23.71  23.71  23.71  23.71\n",
       "...        ...    ...    ...    ...\n",
       "18925.0  44.41  44.95  43.57  44.73\n",
       "18926.0  44.51  46.23  44.14  45.53\n",
       "18927.0  45.45  45.52  43.50  44.57\n",
       "18928.0  44.00  44.78  42.38  42.89\n",
       "18929.0  42.89  43.88  42.59  43.83\n",
       "\n",
       "[974 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16.19]\n",
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      " [22.97]]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:512, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[973 - 300:, 4:5].to_numpy()\n",
    "print(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 100)               20800     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 20,901\n",
      "Trainable params: 20,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=3\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "8/8 [==============================] - 4s 88ms/step - loss: 0.1013 - val_loss: 0.1348\n",
      "Epoch 2/30\n",
      "8/8 [==============================] - 0s 14ms/step - loss: 0.0685 - val_loss: 0.0918\n",
      "Epoch 3/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0452 - val_loss: 0.0573\n",
      "Epoch 4/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0270 - val_loss: 0.0296\n",
      "Epoch 5/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0142 - val_loss: 0.0111\n",
      "Epoch 6/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0096 - val_loss: 0.0047\n",
      "Epoch 7/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0089 - val_loss: 0.0038\n",
      "Epoch 8/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0077 - val_loss: 0.0043\n",
      "Epoch 9/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0064 - val_loss: 0.0043\n",
      "Epoch 10/30\n",
      "8/8 [==============================] - 0s 14ms/step - loss: 0.0055 - val_loss: 0.0035\n",
      "Epoch 11/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0046 - val_loss: 0.0022\n",
      "Epoch 12/30\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.0037 - val_loss: 0.0017\n",
      "Epoch 13/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0032 - val_loss: 0.0015\n",
      "Epoch 14/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0030 - val_loss: 0.0014\n",
      "Epoch 15/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0030 - val_loss: 0.0014\n",
      "Epoch 16/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0030 - val_loss: 0.0014\n",
      "Epoch 17/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0029 - val_loss: 0.0013\n",
      "Epoch 18/30\n",
      "8/8 [==============================] - 0s 15ms/step - loss: 0.0029 - val_loss: 0.0014\n",
      "Epoch 19/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0028 - val_loss: 0.0013\n",
      "Epoch 20/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0028 - val_loss: 0.0013\n",
      "Epoch 21/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0028 - val_loss: 0.0012\n",
      "Epoch 22/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0027 - val_loss: 0.0012\n",
      "Epoch 23/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0027 - val_loss: 0.0012\n",
      "Epoch 24/30\n",
      "8/8 [==============================] - 0s 13ms/step - loss: 0.0027 - val_loss: 0.0012\n",
      "Epoch 25/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0026 - val_loss: 0.0012\n",
      "Epoch 26/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0026 - val_loss: 0.0012\n",
      "Epoch 27/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0025 - val_loss: 0.0012\n",
      "Epoch 28/30\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.0025 - val_loss: 0.0011\n",
      "Epoch 29/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0025 - val_loss: 0.0011\n",
      "Epoch 30/30\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.0025 - val_loss: 0.0011\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 100)               20800     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 20,901\n",
      "Trainable params: 20,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title('中航西飞')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 2.94523\n",
      "均方根误差: 1.71617\n",
      "平均绝对误差: 1.20687\n",
      "R2: 0.94595\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
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